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Rounding

Specifications

  • Data Type: Continuous numeric data

Description

Often when dealing with continuous numeric attributes like proportions or percentages, we may not need the raw values to have a high amount of precision. Hence it often makes sense to round off these high-precision percentages into numeric integers. These integers can then be directly used as raw values or even as categorical (discrete-class-based) features. Let's try applying this concept in a dummy dataset depicting store items and their popularity percentages.

Example

items_popularity = pd.read_csv('datasets/item_popularity.csv', encoding='utf-8')

items_popularity['popularity_scale_10'] = np.array(
    np.round((items_popularity['pop_percent'] * 10)),
    dtype='int'
)

items_popularity['popularity_scale_100'] = np.array(
    np.round((items_popularity['pop_percent'] * 100)),
    dtype='int'
)

items_popularity

We tried two forms of rounding. The features depict the item popularities now both on a scale of 1–10 and on a scale of 1–100. You can use these values both as numerical or categorical features based on the scenario and problem.